industry_collect

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It does disclose a key behavioral trait: the tool writes data into a local SQLite database, which signals persistence and mutation. It also names the upstream sources (同花顺+巨潮). However, it does not disclose whether existing data is overwritten or appended, whether network access is required, whether the operation is idempotent, or how long it may take.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence that front-loads the action and includes the destination and sources. There is no filler or redundant restatement of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (no parameters) and existence of an output schema, the description covers the core essentials: what it triggers, what data domain is involved, where it writes, and which sources are used. Missing context includes when this collection should run relative to sibling collection tools and what the output schema will actually contain, though the latter is partially covered by the output schema itself.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and 100% schema description coverage, so there is nothing for the description to add about argument semantics. The baseline of 4 applies because no parameter documentation is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('触发行业数据采集') and the resource affected (industry data) plus the destination (local SQLite database) and data sources (同花顺+巨潮). It is not a tautology and reads as a legitimate verb+resource statement. However, it does not explicitly distinguish itself from closely named siblings like industry_daily_collect or industry_sw_daily.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus the many sibling collection tools, no prerequisites, no frequency recommendations, and no mention of whether it should be run before querying industry data. An agent would have to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.